Spend $50 to get a free movie!
Edition: 2nd 2018
30 day, 100% satisfaction guarantee!
This item will ship on
Friday, March 31.
Rush Rewards U
You have reached 400 XP and carrot coins. That is the daily max!
"Mathematical Statistics with Resampling and R is a great resource for intermediate and advanced statistics students who want to achieve an in-depth understanding of resampling techniques backed by practical implementation." (Book Pleasures, 2012) "It is highly recommended to someone with a good background in mathematics, probability, and basic statistics who wants to learn about the theory and about resampling and how it relates to traditional methods, and how to implement resampling in R. The book is also a wonderful source of simulations to support the teaching of statistics." (Journal of Biopharmaceutical Statistics, 2011) "It is less demanding mathematically, more applied in its… emphasis, and more modern in content than the usual book, which makes it a good choice if you want a modern applied book at the level of Larsen and Marx (1986)."- George W. Cobb, Mount Holyoke College Department of Mathematics and Statistics (Chilean Journal of Statistics, 1 April 2011) Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Continuing to strike a balance between theory, computing, and applications, in the new edition the authors explore additional topics including consulting, paired t test, hypothesis testing, and regression assumptions, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints, making it an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.